docstr: Returns a dictionnary of all metrics and losses being tracked
          
docstr_tok: ["Returns","a","dictionnary","of","all","metrics","and","losses","being","tracked"]
code: def get_metrics(self, verbose=False) -> Dict[str, Any]:
      """Returns a dictionnary of all metrics and losses being tracked"""
      metrics = super().get_metrics(verbose)
  
      if verbose:
          metrics[
              "{}_iou_per_class".format(self._stage)
          ] = self._iou_per_class
          if self._vote_miou:
              metrics[
                  "{}_full_vote_miou".format(self._stage)
              ] = self._full_vote_miou
              metrics[
                  "{}_vote_miou".format(self._stage)
              ] = self._vote_miou
      return metrics
  
file_path: torch-points3d/torch_points3d/metrics/s3dis_tracker.py
imports: from typing import Dict, Any
  import logging
  import torch
  from torch_geometric.nn.unpool import knn_interpolate
  from torch_points3d.metrics.confusion_matrix import ConfusionMatrix
  from torch_points3d.metrics.segmentation_tracker import SegmentationTracker
  from torch_points3d.metrics.base_tracker import BaseTracker, meter_value
  from torch_points3d.datasets.segmentation import IGNORE_LABEL
  from torch_points3d.core.data_transform import SaveOriginalPosId
  from torch_points3d.models import model_interface
label_desc: Use image node_id 6 for calling the S3DISTracker obj's underlying member method code with example usage: obj.get_metrics(verbose) and returns: metrics